Medical images are produced by many types of medical equipment such as magnetic resonance imaging (MRI) scanners, computed tomography (CT) scanners, single photon emission computed tomography (SPECT) scanners, positron emission tomography (PET) scanners and ultra sound scanners. Many such medical images are of three or higher dimensions and in order for clinicians to visualize those images they need to be rendered onto two-dimensional displays. This rendering process is typically computationally expensive, complex and time consuming. In addition, the process needs to be accurate to enable medical diagnoses to be made.
The large quantity of data contained within such medical images means that a clinician or other user needs to spend a significant amount of time searching for the relevant part of the image. For example, in the case of a medical scan a clinician can spend a significant amount of time manually searching for the relevant part of the body (e.g. heart, kidney, blood vessels) before looking for certain features (e.g. signs of cancer or anatomical anomalies) that can help a diagnosis. Typically the clinician is required to operate sliders to navigate the visual data. In addition, it is often difficult for users to remove clutter from the image and focus on the regions of interest. In order to obtain diagnostic clarity for example, a clinician may need to manually resize and center an image, manually select different transfer functions used in the rendering process, manually select different clipping planes by trial and error and so on. This process may often take the majority of the time taken to make the complete diagnosis from the medical image. Indeed, for organs that vary in location widely between patients such as the spleen and organs which are non-normal in size and shape due to anomalies, injuries or disease this process may be very time consuming.
Some techniques exist for the automatic detection and recognition of organs in medical images, which can reduce the time spent manually searching an image. For example, geometric methods include template matching and convolution techniques. For medical images, geometrically meaningful features can, for example, be used for the segmentation of the aorta and the airway tree. However, such geometric approaches have problems capturing invariance with respect to deformations (e.g. due to pathologies), changes in viewing geometry (e.g. cropping) and changes in intensity. In addition, they do not generalize to highly deformable structures such as some blood vessels.
Another example is an atlas-based technique. An atlas is a hand-classified image, which is mapped to a subject image by deforming the atlas until it closely resembles the subject. This technique is therefore dependent on the availability of good atlases. In addition, the conceptual simplicity of such algorithms is in contrast to the requirement for accurate, deformable algorithms for registering the atlas with the subject. In medical applications, a problem with n-dimensional registration is in selecting the appropriate number of degrees of freedom of the underlying geometric transformation; especially as it depends on the level of rigidity of each organ/tissue. In addition, the optimal choice of the reference atlas can be complex (e.g. selecting separate atlases for an adult male body, a child, or a woman, each of which can be contrast enhanced or not). Atlas-based techniques can also be computationally inefficient.
The embodiments described below are not limited to implementations which solve any or all of the disadvantages of known medical image rendering systems.